Prediction of various blood group systems using Korean whole-genome sequencing data

Abstract

© 2022 Hyun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Aims This study established blood group analysis methods using whole-genome sequencing (WGS) data and conducted blood group analyses to determine the domestic allele frequency using public data from the Korean whole sequence analysis of the Korean Reference Genome Project conducted by the Korea Disease Control and Prevention Agency (KDCA). Materials and methods We analyzed the differences between the human reference sequences (hg19) and the conventional reference cDNA sequences of blood group genes using the Clustal Omega website, and established blood group analysis methods using WGS data for 41 genes, including 39 blood group genes involved in 36 blood group antigens, as well as the GATA1 and KLF1 genes, which are erythrocyte-specific transcription factor genes. Using CLC genomics Workbench 11.0 (Qiagen, Aarhus, Denmark), variant analysis was performed on these 41 genes in 250 Korean WGS data sets, and each blood groups genotype was predicted. The frequencies for major alleles were also investigated and compared with data from the Korean rare blood program (KRBP) and the Erythrogene database (East Asian and all races). Results Among the 41 blood group-related genes, hg19 showed variants in the following genes compared to the conventional reference cDNA: GYPA, RHD, RHCE, FUT3, ACKR1, SLC14A1, ART4, CR1, and GCNT2. Among 250 WGS data sets from the Korean Reference Genome Project, 70.6 variants were analyzed in 205 samples; 45 data samples were excluded due to having no variants. In particular, the FUT3, GNCT2, B3GALNT1, CR1, and ACHE genes contained numerous variants, with averages of 21.1, 13.9, 13.4, 9.6, and 7.0, respectively. Except for some blood groups, such as ABO and Lewis, for which it was difficult to predict the alleles using only WGS data, most alleles were successfully predicted in most blood groups. A comparison of allele frequencies showed no significant differences compared to the KRBP data, but there were differences compared to the Erythrogene data for the Lutheran, Kell, Duffy, Yt, Scianna, Landsteiner-Wiener, and Cromer blood group systems. Numerous minor blood group systems that were not available in the KRBP data were also included in this study. Conclusions We successfully established and performed blood group analysis using Korean public WGS data. It is expected that blood group analysis using WGS data will be performed more frequently in the future and will contribute to domestic data on blood group allele frequency and eventually the supply of safe blood products.